probe_match: Probe matching

In pomp: Statistical Inference for Partially Observed Markov Processes

Probe matching

Description

Estimation of parameters by maximum synthetic likelihood

Usage

## S4 method for signature 'data.frame'
probe_objfun(
  data,
  est = character(0),
  fail.value = NA,
  probes,
  nsim,
  seed = NULL,
  params,
  rinit,
  rprocess,
  rmeasure,
  partrans,
  ...,
  verbose = getOption("verbose", FALSE)
)

## S4 method for signature 'pomp'
probe_objfun(
  data,
  est = character(0),
  fail.value = NA,
  probes,
  nsim,
  seed = NULL,
  ...,
  verbose = getOption("verbose", FALSE)
)

## S4 method for signature 'probed_pomp'
probe_objfun(
  data,
  est = character(0),
  fail.value = NA,
  probes,
  nsim,
  seed = NULL,
  ...,
  verbose = getOption("verbose", FALSE)
)

## S4 method for signature 'probe_match_objfun'
probe_objfun(
  data,
  est,
  fail.value,
  seed = NULL,
  ...,
  verbose = getOption("verbose", FALSE)
)

Arguments

data	either a data frame holding the time series data, or an object of class ‘pomp’, i.e., the output of another pomp calculation. Internally, data will be coerced to an array with storage-mode double.
est	character vector; the names of parameters to be estimated.
fail.value	optional numeric scalar; if non-NA, this value is substituted for non-finite values of the objective function. It should be a large number (i.e., bigger than any legitimate values the objective function is likely to take).
probes	a single probe or a list of one or more probes. A probe is simply a scalar- or vector-valued function of one argument that can be applied to the data array of a ‘pomp’. A vector-valued probe must always return a vector of the same size. A number of useful probes are provided with the package: see basic probes.
nsim	the number of model simulations to be computed.
seed	integer. When fitting, it is often best to fix the seed of the random-number generator (RNG). This is accomplished by setting seed to an integer. By default, seed = NULL, which does not alter the RNG state.
params	optional; named numeric vector of parameters. This will be coerced internally to storage mode double.
rinit	simulator of the initial-state distribution. This can be furnished either as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library. Setting rinit=NULL sets the initial-state simulator to its default. For more information, see rinit specification.
rprocess	simulator of the latent state process, specified using one of the rprocess plugins. Setting rprocess=NULL removes the latent-state simulator. For more information, see rprocess specification for the documentation on these plugins.
rmeasure	simulator of the measurement model, specified either as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library. Setting rmeasure=NULL removes the measurement model simulator. For more information, see rmeasure specification.
partrans	optional parameter transformations, constructed using parameter_trans. Many algorithms for parameter estimation search an unconstrained space of parameters. When working with such an algorithm and a model for which the parameters are constrained, it can be useful to transform parameters. One should supply the partrans argument via a call to parameter_trans. For more information, see parameter_trans. Setting partrans=NULL removes the parameter transformations, i.e., sets them to the identity transformation.
...	additional arguments are passed to pomp.
verbose	logical; if TRUE, diagnostic messages will be printed to the console.

Details

In probe-matching, one attempts to minimize the discrepancy between simulated and actual data, as measured by a set of summary statistics called probes. In pomp, this discrepancy is measured using the “synthetic likelihood” as defined by Wood (2010).

Value

probe_objfun constructs a stateful objective function for probe matching. Specifically, probe_objfun returns an object of class ‘probe_match_objfun’, which is a function suitable for use in an optim-like optimizer. In particular, this function takes a single numeric-vector argument that is assumed to contain the parameters named in est, in that order. When called, it will return the negative synthetic log likelihood for the probes specified. It is a stateful function: Each time it is called, it will remember the values of the parameters and its estimate of the synthetic likelihood.

Note for Windows users

Some Windows users report problems when using C snippets in parallel computations. These appear to arise when the temporary files created during the C snippet compilation process are not handled properly by the operating system. To circumvent this problem, use the cdir and cfile options to cause the C snippets to be written to a file of your choice, thus avoiding the use of temporary files altogether.

Important Note

Since pomp cannot guarantee that the final call an optimizer makes to the function is a call at the optimum, it cannot guarantee that the parameters stored in the function are the optimal ones. Therefore, it is a good idea to evaluate the function on the parameters returned by the optimization routine, which will ensure that these parameters are stored.

Warning! Objective functions based on C snippets

If you use C snippets (see Csnippet), a dynamically loadable library will be built. As a rule, pomp functions load this library as needed and unload it when it is no longer needed. The stateful objective functions are an exception to this rule. For efficiency, calls to the objective function do not execute pompLoad or pompUnload: rather, it is assumed that pompLoad has been called before any call to the objective function. When a stateful objective function using one or more C snippets is created, pompLoad is called internally to build and load the library: therefore, within a single R session, if one creates a stateful objective function, one can freely call that objective function and (more to the point) pass it to an optimizer that calls it freely, without needing to call pompLoad. On the other hand, if one retrieves a stored objective function from a file, or passes one to another R session, one must call pompLoad before using it. Failure to do this will typically result in a segmentation fault (i.e., it will crash the R session).

Author(s)

Aaron A. King

References

\Kendall

1999

\Wood

2010

See Also

optim subplex nloptr

More on methods based on summary statistics: abc(), basic_probes, nlf, probe(), spect(), spect_match

More on pomp estimation algorithms: abc(), bsmc2(), estimation_algorithms, mif2(), nlf, pmcmc(), pomp-package, spect_match

More on maximization-based estimation methods: mif2(), nlf, spect_match, traj_match

Examples

  gompertz() -> po
  
  ## A list of probes:
  plist <- list(
    mean=probe_mean("Y",trim=0.1,transform=sqrt),
    sd=probe_sd("Y",transform=sqrt),
    probe_marginal("Y",ref=obs(po)),
    probe_acf("Y",lags=c(1,3,5),type="correlation",transform=sqrt),
    probe_quantile("Y",prob=c(0.25,0.75),na.rm=TRUE)
  )

  ## Construct the probe-matching objective function.
  ## Here, we just want to estimate 'K'.
  po |>
    probe_objfun(probes=plist,nsim=100,seed=5069977,
      est="K") -> f

  ## Any numerical optimizer can be used to minimize 'f'.
  if (require(subplex)) {

    subplex(fn=f,par=0.4,control=list(reltol=1e-5)) -> out

  } else {

    optim(fn=f,par=0.4,control=list(reltol=1e-5)) -> out

  }

  ## Call the objective one last time on the optimal parameters:
  f(out$par)
  coef(f)

  ## There are 'plot' and 'summary' methods:
  f |> as("probed_pomp") |> plot()
  f |> summary()

  ## One can convert an objective function to a data frame:
  f |> as("data.frame") |> head()
  f |> as("probed_pomp") |> as("data.frame") |> head()

  f |> probe() |> plot()

  ## One can modify the objective function with another call
  ## to 'probe_objfun':

  f |> probe_objfun(est=c("r","K")) -> f1
  optim(fn=f1,par=c(0.3,0.3),control=list(reltol=1e-5)) -> out
  f1(out$par)
  coef(f1)

pomp documentation built on Sept. 13, 2024, 1:08 a.m.